DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0.

Catalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorption site structures. Machine learning reduces the cost for modelling those sites with the aid of descriptors. We analysed the performance of state-of-the-art structural descriptors Smooth Overlap of Atomic Positions, Many-Body Tensor Representation and Atom-Centered Symmetry Functions while predicting the hydrogen adsorption (free) energy on the surface of nanoclusters. The 2D-material molybdenum disulphide and the alloy copper-gold functioned as test systems. Potential energy scans of hydrogen on the cluster surfaces were conducted to compare the accuracy of the descriptors in kernel ridge regression. By having recourse to data sets of 91 molybdenum disulphide clusters and 24 copper-gold clusters, we found that the mean absolute error could be reduced by machine learning on different clusters simultaneously rather than separately. The adsorption energy was explained by the local descriptor Smooth Overlap of Atomic Positions, combining it with the global descriptor Many-Body Tensor Representation did not improve the overall accuracy. We concluded that fitting of potential energy surfaces could be reduced significantly by merging data from different nanoclusters.

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We present a versatile parallelised genetic algorithm, which is able to perform global optimisation from first principles for pure and mixed free clusters in the gas phase, supported on surfaces or in the presence of one or several atomic or molecular species (ligands or adsorbates).

The global optimization of nanoparticles, such as pure or bimetallic metal clusters, has become a very important and sophisticated research field in modern nanoscience. The possibility of using more rigorous quantum chemical first principle methods during the global optimization has been facilitated by the development of more powerful computer hardware as well as more efficient algorithms. In this review, recent advances in first principle global optimization methods are described, with the main focus on genetic algorithms coupled with density functional theory for optimizing sub-nanometre metal clusters and nanoalloys.

In the present work, the optical response of isolated (CdSe) n + clusters with n = 3–6 is probed by measuring the photodissociation cross section in the photon energy range ℏω = 1.9–4.9 eV. In this joint experimental and theoretical study, the experimental observations are analyzed with time-dependent density functional theory and equation-of-motion coupled cluster theory. Structural candidates for the time-dependent excited-state calculations are obtained via global optimization by employing a genetic algorithm. The combined experimental and theoretical approach allows the discrimination of cluster geometries in the molecular beam experiments. From n ≥ 5, three-dimensional structures are found. Already for n = 6, light absorption in the red spectral range is observed. This observation is discussed with respect to the size dependence of the optical behavior of finite systems taking experimental and theoretical work on bare and ligated CdSe clusters and nanoparticles into account. Particularly, the influence of the net charge and ligands is considered. This allows a detailed discussion of the size-dependent evolution of the optical properties starting from molecular species over to nanoclusters and nanoparticles and finally to bulk CdSe.

Quantum-chemically supported electric beam deflection and photodissociation spectroscopy reveal the shape evolution and optical absorption of small Sn clusters with focus on the rotational, vibrational, multiphoton and dissociation characteristics.